Image and Video Generation in Neiron AI: What to Prepare Before Making a Request

Image generation and video generation require more preparation than a regular text request. If a user writes “make a beautiful picture” or “make a video for an ad,” the result will depend on random model interpretations. A more reliable approach is to describe in advance the goal, audience, scene, format, constraints, and evaluation criteria. In Neiron AI, there are separate public endpoints /images and /videos for such scenarios, as well as tariffs and generation packages that need to be considered before running a series of experiments.

For images, the fact-check database lists Nano Banana, Nano Banana Pro, and GPT Image 2. Nano Banana is described as a model for quick images based on descriptions and variants from photos. Nano Banana Pro is associated with quality modes for 1K, 2K, or 4K. GPT Image 2 creates and edits images, including scenarios with a single source photo. These are acceptable facts. It is inadmissible to promise that any model will create a commercially ready banner, increase conversion, or replace a designer. It is safer to explain how to prepare a request and how to evaluate the result.

A good image request contains the object, action, setting, style, angle, color, format, and prohibitions. For example: “create an image for the cover of an article about an AI platform: a desk, laptop, abstract model cards, calm blue and purple tones, no third-party brand logos, horizontal format.” If an image based on a photo is needed, specify what can be changed and what must remain. If the model supports quality selection, choose it deliberately rather than after ten random attempts.

For video, the database lists Veo 3.1, Seedance 2.0, Grok Imagine, Wan 2.6, and Kling Motion. Veo 3.1 is used for short videos with sound and speech. Seedance 2.0 supports quick videos and scenarios with references. Grok Imagine is used for fast creative videos. Wan 2.6 is suitable for videos in different frame formats and lengths. Kling Motion is used for motion templates and photo dance scenarios. These descriptions help choose a direction but do not promise a specific visual result.

A video request should describe the sequence. Indicate the first frame, action, camera, object movement, length, mood, background, sound or speech if supported by the scenario. For a social media clip, it is helpful to decide in advance whether a vertical format, a single character, text overlays, or product demonstration is needed. If the goal is marketing material, do not claim in the article “high-converting.” Better to write: prepare several variants, show them to the editor, check meaning, legal restrictions, brand alignment, and technical quality.

Before generating media, it is worth checking the limits. The subscriptions Neuron Lite, Neuron Max, and Neuron Mega Max differ in the number of images per day and videos per month. There are one-time generation packages for images and videos. For intensive work with Nano Banana, there are separate plans Nano Banana and Nano Banana x2. This matters because media generations usually consume faster than text requests. First formulate the idea in text, then make one trial version, correct the request, and only then run a series.

A separate issue is source materials. If you upload a photo, text, file, or other content, ensure you have the right to use it in AI tools. The privacy database mentions user content, attached files, generation results, and AI model providers. Therefore, do not send personal documents, client commercial materials, or images of people without understanding rights and consents. For a public article, it is sufficient to honestly warn: check rights to source material and the result before publication.

After generation, evaluate the result using a checklist. For images: does the object match, are there extra details, is the format suitable, is the composition readable, are there any third-party logos, is in-image text correct. For video: is the first frame clear, is the motion smooth, are there undesirable artifacts, does the length match, does the video conflict with the task. If the result goes into advertising or a public channel, add a manual editorial check.

In the article, it is useful to link media scenarios to internal pages. /images leads to image generation, /videos to video generation, /pricing to limits and packages, /support to questions about payment, account, and generations, /news/articles to future guides. This makes the material practical and avoids repeating a raw draft.

Mini-template for editor and author

Before launching image generation, fill in five lines: image purpose, object, setting, format, constraints. For example, for a cover image, the purpose could be “explain the topic of the AI platform,” object – “laptop and model cards,” setting – “workspace without recognizable brands,” format – “horizontal cover,” constraints – “no real people’s faces and no third-party logos.” Such a template helps avoid wasting generations on model guesses.

For video, use a similar scheme but add movement: first frame, action, scene change, final frame, sound or speech. If the scenario involves a product, do not ask the model to show an interface that is not in the approved public mockup. Better to use an abstract demonstration of the idea, and prepare exact screenshots separately through product design and legal review.

After obtaining the result, save not only the file but also the request. This allows you to repeat a successful generation, explain to the editor why the result looks as it does, and fix the next version faster. For public publication, add a check: is there distorted text, accidental logos, erroneous interfaces, unverified medical, financial, or legal hints. If in doubt, do not publish the result and contact the editor or content owner.

FAQ

Can I use external images from an export file? In this editorial review, they are not used: rights and relevance have not been verified through the platform’s media checks.

Which model is suitable for images? Neiron AI lists Nano Banana, Nano Banana Pro, and GPT Image 2. The choice depends on the scenario and availability in the interface.

How can I avoid wasting generations? First prepare a text description, then make a trial version, correct the request, and only after that run a series.

How to save successful requests

After a good generation, save not only the final file but also the request itself: scene description, style, format, constraints, and edits after the first attempt. This helps to repeat the approach later and understand what influenced the result. For images, it is useful to store the composition and object details separately; for video, store the movement, length, background, and final frame. Such an archive does not guarantee the same result but makes working with generations calmer and more predictable.